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|---|---|---|---|---|
| Rmd | 2a9484f | Andy Beck | 2025-02-11 | wflow_publish("analysis/three_pos_results.Rmd") |
| html | 05d7220 | Andy Beck | 2025-02-04 | Build site. |
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In this document we will evaluate the results from three-way interaction models. Our primary interest is contrasting their strength to single-position and two-way interactions.
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggpubfigs) # functions for color blind friendly palettes
Attaching package: 'ggpubfigs'
The following object is masked from 'package:ggplot2':
theme_grey
library(reactable)
base_dir <- "output"
three_dir <- paste0(base_dir, "/three_pos") # template: base_dir/three_pos/{population}/{subtype}.csv
two_dir <- paste0(base_dir, "/two_pos") # template: base_dir/two_pos/{population}/{subtype}.csv
single_dir <- paste0(base_dir, "/single_pos") # template: base_dir/single_pos/{population}/{subtype}.csv
subtypes <- c("AT_CG", "AT_GC", "AT_TA",
"GC_AT", "GC_TA", "GC_CG",
"cpg_GC_AT", "cpg_GC_TA", "cpg_GC_CG")
read_single <- function(res_dir, pop, subtype, tp=FALSE){
f_name <- paste0(res_dir, "/", pop, "/", subtype, ".csv")
if(tp){
return(read_csv(f_name, show_col_types = F, col_names = c("dev","singletons","controls","rp1","rp2")))
}
return(read_csv(f_name, show_col_types = FALSE))
}
read_all <- function(single_dir, two_dir, three_dir, pop, subtype){
s_df <- read_single(single_dir, pop, subtype)
two_df <- read_single(two_dir, pop, subtype, tp=T)
three_df <- read_single(three_dir, pop, subtype)
# compute re
three_df <- three_df %>%
mutate(re = dev / (2 * (singletons + controls)))
two_df <- two_df %>%
mutate(re = dev / (2 * (singletons + controls)))
s_df <- s_df %>%
mutate(re = dev / (2 * (singletons + controls)))
# rank by re
three_df <- three_df %>%
mutate(class_rank = dense_rank(desc(re)))
two_df <- two_df %>%
mutate(class_rank = dense_rank(desc(re)))
s_df <- s_df %>%
mutate(class_rank = dense_rank(desc(re)))
# unify structure: dev, offsets, re, class, class_rank
three_df <- three_df %>%
rowwise() %>%
mutate(offset = paste0(rp1, "_", rp2, "_", rp3)) %>%
ungroup() %>%
mutate(class = "3-pos") %>%
select(dev, offset, re, class ,class_rank)
two_df <- two_df %>%
mutate(class = "2-pos") %>%
rowwise() %>%
mutate(offset = paste0(rp1, "_", rp2)) %>%
ungroup() %>%
select(dev, offset, re, class, class_rank)
s_df <- s_df %>%
mutate(class = "1-pos",
offset = as.character(offset)) %>%
select(dev, offset, re, class, class_rank)
final_df <- bind_rows(s_df, two_df, three_df)
return(final_df)
}
subtype_print_names <- function(st){
if(str_starts(st, "AT")){
return(paste0("A → ", str_sub(st, 4, 4)))
} else if(str_starts(st, "GC")){
return(paste0("C → ", str_sub(st, 5, 5)))
} else{
return(paste0("CpG → ", str_sub(st, 9, 9), "pG"))
}
}
# function will print both table and plot
plot_ranked_re <- function(single_dir, two_dir, three_dir, pop, subtype, rank_lim = 20){
df <- read_all(single_dir, two_dir, three_dir, pop, subtype)
max_re <- max(df$re)
df$pct_max <- df$re / max_re
p <- df %>%
filter(class_rank <= rank_lim) %>%
mutate(class2 = recode(class, "1-pos" = "Single", "2-pos" = "Two-Way", "3-pos" = "Three-Way")) %>%
ggplot(aes(x = class_rank, y = pct_max, colour = class2, shape = class2)) +
geom_point(size = 2.5) +
scale_colour_manual(values=friendly_pal("contrast_three"), name = "Type", breaks=c("Single","Two-Way","Three-Way")) +
scale_shape_discrete(name = "Type", breaks=c("Single","Two-Way","Three-Way")) +
theme_classic(base_size = 12, base_family = "Helvetica") +
theme(legend.position = "inside",
legend.position.inside = c(.8, .8)) +
ylab("% of Max RE") +
xlab(paste0("Within-class Rank: ", subtype_print_names(subtype)))
return(list(df=df, fig=p))
}
Let’s look at mtcars instead of local sequence context non-sense…
reactable(mtcars[1:4, 1:5])
And now that the reactable stuff has been attached to our document, let’s look at local sequence context results :)

| Version | Author | Date |
|---|---|---|
| 05d7220 | Andy Beck | 2025-02-04 |
sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3; LAPACK version 3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reactable_0.4.4 ggpubfigs_0.0.1 lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.49 bslib_0.8.0 htmlwidgets_1.6.4
[5] processx_3.8.4 callr_3.7.6 tzdb_0.4.0 vctrs_0.6.5
[9] tools_4.4.2 crosstalk_1.2.1 ps_1.8.1 generics_0.1.3
[13] parallel_4.4.2 fansi_1.0.6 pkgconfig_2.0.3 lifecycle_1.0.4
[17] farver_2.1.2 compiler_4.4.2 git2r_0.33.0 munsell_0.5.1
[21] getPass_0.2-4 httpuv_1.6.15 htmltools_0.5.8.1 sass_0.4.9
[25] yaml_2.3.10 later_1.3.2 pillar_1.9.0 crayon_1.5.3
[29] jquerylib_0.1.4 whisker_0.4.1 cachem_1.1.0 tidyselect_1.2.1
[33] digest_0.6.37 stringi_1.8.4 labeling_0.4.3 rprojroot_2.0.4
[37] fastmap_1.2.0 grid_4.4.2 archive_1.1.8 colorspace_2.1-1
[41] cli_3.6.3 magrittr_2.0.3 utf8_1.2.4 withr_3.0.2
[45] reactR_0.6.1 scales_1.3.0 promises_1.3.0 bit64_4.6.0-1
[49] timechange_0.3.0 rmarkdown_2.29 httr_1.4.7 bit_4.5.0.1
[53] hms_1.1.3 evaluate_1.0.1 knitr_1.49 rlang_1.1.4
[57] Rcpp_1.0.13-1 glue_1.8.0 rstudioapi_0.17.1 vroom_1.6.5
[61] jsonlite_1.8.9 R6_2.5.1 fs_1.6.5